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Usability explained: Examples and advice for successful user guidance

By Marino Casucci

To help online visitors with orientation and to speed up a user’s search for the right product, most web shops use an auto-suggest function. This means, having typed the first few characters into the search field a list of word suggestions will appear.

Today’s searching technologies allow retailers to play off further data such as pricing, customer ratings and brand logos in the list of suggestions. In this way, access to a whole range of products is additionally supported. However, shops still need to be careful when using these techniques as an overload of text and pictures will only lead to confusion among users.

So which information is relevant and which is not? Basically, it’s important to have a focus: Depending on the branch, product range, target group and business strategy, ecommerce managers should consider which additional suggest elements make sense to display. Beyond suggest elements that help most of the visitors – eg. category or product suggestions and prices – this could be tutorials or icons like “new” or “sale”.

Furthermore, it should be carefully considered which suggestions should appear top of the list: Those with the highest search volume? Those relevant for a current promotion? Or those with the highest profit margin? The answer depends on the company goals and on the image that the website is trying to portray. For an online retailer focusing on quality, it does not make sense to display the cheapest products at the top of the list of suggestions. Instead, products with very good customer ratings would fit better for this retailer. Popular European retailers such as HSE24, Rossmann and OBI demonstrate how the suggest function supports merchandising campaigns and content strategies.

How HSE24 and Rossmann push products through commercials

Articles that are relevant today are likely to be old tomorrow – and vice versa. In order to react on such trends teleshopping channel HSE24 promotes daily product campaigns on TV as well as in its web shop (www.hse24.de). The auto-suggest function makes sure that the advertised products are shown in a special way, with a highlighted price and a thumbnail.

Clever merchandising: With thumbnails and coloured prices HSE24 highlights chosen products at the top of the list in the suggest menu.

Also, drugstore chain Rossmann (www.rossmannversand.de) uses auto-suggest to leverage sales of campaign products. Articles from the current catalogue appear on top of the list; afterwards the best-selling products are shown.

In contrast to HSE24, Rossmann still shows the initial price of reduced products – softened and crossed out. On the one hand, this could disturb some online visitors who only want to browse, but on the other hand this information helps many users with a concrete buying intention to come to a decision.

OBI inspires online visitors to buy in local branches

Google studies prove the Research Online Purchase Offline (ROPO)-effect: 38% of all offline buyers carry out product research before making the purchase in local branches. In doing so, ROPO customers have a higher average purchasing volume because of additional purchases. The do-it-yourself chain OBI supports this effect specifically, already presenting tutorials to the online visitors in the suggest menu. In this way the customer’s need for information is optimally satisfied.

Whether it’s a video tutorial, a blog article or consultant advice, information is easy to find for OBI’s online user. This ultimately drives customers to visit a local branch, where personal guidance brings the buying decision forward.

Supporting their own shop strategy as well as providing a unique shopping experience is possible for ecommerce managers if they use the right data in the right order in the auto-suggest function. There are always recommendations that are valuable for almost every shop. Our Usability Study in co-operation with the innovative services marketing students of Pforzheim Business School shows that in suggest menus, online users tend to be guided by product prices and classification of categories.